Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. Our experiments showed that self-training with Noisy Student and EfficientNet can achieve an accuracy of 87.4% which is 1.9% higher than without Noisy Student. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. Train a classifier on labeled data (teacher). Our experiments showed that self-training with Noisy Student and EfficientNet can achieve an accuracy of 87.4% which is 1.9% higher than without Noisy Student. [68, 24, 55, 22]. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. Especially unlabeled images are plentiful and can be collected with ease. As can be seen from the figure, our model with Noisy Student makes correct predictions for images under severe corruptions and perturbations such as snow, motion blur and fog, while the model without Noisy Student suffers greatly under these conditions. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. While removing noise leads to a much lower training loss for labeled images, we observe that, for unlabeled images, removing noise leads to a smaller drop in training loss. Selected images from robustness benchmarks ImageNet-A, C and P. Test images from ImageNet-C underwent artificial transformations (also known as common corruptions) that cannot be found on the ImageNet training set. Self-Training With Noisy Student Improves ImageNet Classification @article{Xie2019SelfTrainingWN, title={Self-Training With Noisy Student Improves ImageNet Classification}, author={Qizhe Xie and Eduard H. Hovy and Minh-Thang Luong and Quoc V. Le}, journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019 . on ImageNet, which is 1.0 Astrophysical Observatory. Self-training first uses labeled data to train a good teacher model, then use the teacher model to label unlabeled data and finally use the labeled data and unlabeled data to jointly train a student model. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Our work is based on self-training (e.g.,[59, 79, 56]). Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. It is expensive and must be done with great care. For example, without Noisy Student, the model predicts bullfrog for the image shown on the left of the second row, which might be resulted from the black lotus leaf on the water. , have shown that computer vision models lack robustness. On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. ImageNet . In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. We then use the teacher model to generate pseudo labels on unlabeled images. Self-Training Noisy Student " " Self-Training . This work introduces two challenging datasets that reliably cause machine learning model performance to substantially degrade and curates an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out- of-dist distribution detection dataset created for ImageNet models. Different kinds of noise, however, may have different effects. The abundance of data on the internet is vast. We then train a larger EfficientNet as a student model on the Noisy Student Training is a semi-supervised learning method which achieves 88.4% top-1 accuracy on ImageNet (SOTA) and surprising gains on robustness and adversarial benchmarks. Code for Noisy Student Training. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. to use Codespaces. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. Our main results are shown in Table1. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher. This paper proposes a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images to improve the performance for a given target architecture, like ResNet-50 or ResNext. w Summary of key results compared to previous state-of-the-art models. The biggest gain is observed on ImageNet-A: our method achieves 3.5x higher accuracy on ImageNet-A, going from 16.6% of the previous state-of-the-art to 74.2% top-1 accuracy. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. on ImageNet ReaL The most interesting image is shown on the right of the first row. We also list EfficientNet-B7 as a reference. The comparison is shown in Table 9. Self-training is a form of semi-supervised learning [10] which attempts to leverage unlabeled data to improve classification performance in the limited data regime. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. Train a larger classifier on the combined set, adding noise (noisy student). Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. Especially unlabeled images are plentiful and can be collected with ease. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. If nothing happens, download Xcode and try again. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative A number of studies, e.g. This work adopts the noisy-student learning method, and adopts 3D nnUNet as the segmentation model during the experiments, since No new U-Net is the state-of-the-art medical image segmentation method and designs task-specific pipelines for different tasks. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, Y. Huang, Y. Cheng, D. Chen, H. Lee, J. Ngiam, Q. V. Le, and Z. Chen, GPipe: efficient training of giant neural networks using pipeline parallelism, A. Iscen, G. Tolias, Y. Avrithis, and O. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. In particular, we first perform normal training with a smaller resolution for 350 epochs. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2.Noisy Student Training extends the idea of self-training and distillation with the use of equal-or-larger student models and noise added to the student during learning. Notice, Smithsonian Terms of However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. To date (2020) we will introduce "Noisy Student Training", which is a state-of-the-art model.The idea is to extend self-training and Distillation, a paper that shows that by adding three noises and distilling multiple times, the student model will have better generalization performance than the teacher model. Works based on pseudo label[37, 31, 60, 1] are similar to self-training, but also suffers the same problem with consistency training, since it relies on a model being trained instead of a converged model with high accuracy to generate pseudo labels. https://arxiv.org/abs/1911.04252. Apart from self-training, another important line of work in semi-supervised learning[9, 85] is based on consistency training[6, 4, 53, 36, 70, 45, 41, 51, 10, 12, 49, 2, 38, 72, 74, 5, 81]. For simplicity, we experiment with using 1128,164,132,116,14 of the whole data by uniformly sampling images from the the unlabeled set though taking the images with highest confidence leads to better results. A tag already exists with the provided branch name. For each class, we select at most 130K images that have the highest confidence. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. When dropout and stochastic depth are used, the teacher model behaves like an ensemble of models (when it generates the pseudo labels, dropout is not used), whereas the student behaves like a single model. In other words, small changes in the input image can cause large changes to the predictions. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. In other words, the student is forced to mimic a more powerful ensemble model. As can be seen, our model with Noisy Student makes correct and consistent predictions as images undergone different perturbations while the model without Noisy Student flips predictions frequently. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. . The algorithm is basically self-training, a method in semi-supervised learning (. Summarization_self-training_with_noisy_student_improves_imagenet_classification. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. Are you sure you want to create this branch? It implements SemiSupervised Learning with Noise to create an Image Classification. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. sign in Noisy Student Training is based on the self-training framework and trained with 4 simple steps: For ImageNet checkpoints trained by Noisy Student Training, please refer to the EfficientNet github. Work fast with our official CLI. Chum, Label propagation for deep semi-supervised learning, D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, Semi-supervised learning with deep generative models, Semi-supervised classification with graph convolutional networks. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. On robustness test sets, it improves ImageNet-A top . We improved it by adding noise to the student to learn beyond the teachers knowledge. We use the same architecture for the teacher and the student and do not perform iterative training. On robustness test sets, it improves Please As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. 3.5B weakly labeled Instagram images. Our experiments showed that self-training with Noisy Student and EfficientNet can achieve an accuracy of 87.4% which is 1.9% higher than without Noisy Student. Med. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. The width. This work systematically benchmark state-of-the-art methods that use unlabeled data, including domain-invariant, self-training, and self-supervised methods, and shows that their success on WILDS is limited. Self-Training with Noisy Student Improves ImageNet Classification Noisy Student Training seeks to improve on self-training and distillation in two ways. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Do better imagenet models transfer better? Self-training with Noisy Student improves ImageNet classification Original paper: https://arxiv.org/pdf/1911.04252.pdf Authors: Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le HOYA012 Introduction EfficientNet ImageNet SOTA EfficientNet On . Their framework is highly optimized for videos, e.g., prediction on which frame to use in a video, which is not as general as our work. For instance, on the right column, as the image of the car undergone a small rotation, the standard model changes its prediction from racing car to car wheel to fire engine. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet This paper reviews the state-of-the-art in both the field of CNNs for image classification and object detection and Autonomous Driving Systems (ADSs) in a synergetic way including a comprehensive trade-off analysis from a human-machine perspective. A. Alemi, Thirty-First AAAI Conference on Artificial Intelligence, C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, EfficientNet: rethinking model scaling for convolutional neural networks, Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results, H. Touvron, A. Vedaldi, M. Douze, and H. Jgou, Fixing the train-test resolution discrepancy, V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), J. Weston, F. Ratle, H. Mobahi, and R. Collobert, Deep learning via semi-supervised embedding, Q. Xie, Z. Dai, E. Hovy, M. Luong, and Q. V. Le, Unsupervised data augmentation for consistency training, S. Xie, R. Girshick, P. Dollr, Z. Tu, and K. He, Aggregated residual transformations for deep neural networks, I. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. We find that Noisy Student is better with an additional trick: data balancing. The best model in our experiments is a result of iterative training of teacher and student by putting back the student as the new teacher to generate new pseudo labels. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. (using extra training data). It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. Use, Smithsonian We iterate this process by putting back the student as the teacher. We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. The performance consistently drops with noise function removed. You signed in with another tab or window. Noisy Student Training extends the idea of self-training and distillation with the use of equal-or-larger student models and noise added to the student during learning. and surprising gains on robustness and adversarial benchmarks. to use Codespaces. Noisy Student (B7, L2) means to use EfficientNet-B7 as the student and use our best model with 87.4% accuracy as the teacher model. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. Our procedure went as follows. In typical self-training with the teacher-student framework, noise injection to the student is not used by default, or the role of noise is not fully understood or justified. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. During the generation of the pseudo The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. Le. When the student model is deliberately noised it is actually trained to be consistent to the more powerful teacher model that is not noised when it generates pseudo labels. 10687-10698). In this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. A common workaround is to use entropy minimization or ramp up the consistency loss. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. Since we use soft pseudo labels generated from the teacher model, when the student is trained to be exactly the same as the teacher model, the cross entropy loss on unlabeled data would be zero and the training signal would vanish. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. At the top-left image, the model without Noisy Student ignores the sea lions and mistakenly recognizes a buoy as a lighthouse, while the model with Noisy Student can recognize the sea lions. Although they have produced promising results, in our preliminary experiments, consistency regularization works less well on ImageNet because consistency regularization in the early phase of ImageNet training regularizes the model towards high entropy predictions, and prevents it from achieving good accuracy. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher. Code is available at https://github.com/google-research/noisystudent. As shown in Table3,4 and5, when compared with the previous state-of-the-art model ResNeXt-101 WSL[44, 48] trained on 3.5B weakly labeled images, Noisy Student yields substantial gains on robustness datasets. Noisy StudentImageNetEfficientNet-L2state-of-the-art. Le, and J. Shlens, Using videos to evaluate image model robustness, Deep residual learning for image recognition, Benchmarking neural network robustness to common corruptions and perturbations, D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song, Distilling the knowledge in a neural network, G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, G. Huang, Y.
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